Hybird Identification of IG baed Fuzzy Model

정보 입자 기반 퍼지 모델의 하이브리드 동정

  • Park, Keon-Jun (Dep. of Electrical Engineering, The University of Suwon.) ;
  • Lee, Dong-Yoon (Dep. of Information and Communications, Joongbu University) ;
  • Oh, Sung-Kwun (Dep. of Electrical Engineering, The University of Suwon.)
  • 박건준 (수원대학교 공과대학 전기공학과) ;
  • 이동윤 (중부대학교 정보통신학과) ;
  • 오성권 (수원대학교 공과대학 전기공학과)
  • Published : 2005.07.18

Abstract

We introduce a hybrid identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally design the IG-based fuzzy model we exploit a hybrid identification through genetic alrogithms(GAs) and Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the seleced input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of HCM clustering help determine the initial paramters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the inital parameters are tuned effectively with the aid of the GAs and the least square method. Numerical example is included to evaluate the performance of the proposed model.

Keywords